This work was supported by the National Basic Research Program of China (“973” Program) under Grant No. 2007CB307101 and No. 2007CB307106.
1 From Cognitive Radio to Cognitive Wireless Network
The term Cognitive Radio (CR) was coined by the Swede, Dr. Mitola, in the late 1990s. During the following 10 years, CR gradually became one of the hot topics in the international wireless industry. CR systems should have capabilities such as sensing, analysis, adjustment, reasoning and learning. In fact, these capabilities of CR form a cognitive cycle. The attributes of CR include:
- Awareness: A CR systems can sense, store, recall and disseminate information related to Radio Frequency (RF) environment, geographical location and context.
- Adjustability: Based on sensed environmental information, a CR system can adjust critical system parameters (e.g. frequency, power and modulation scheme) in response to changes in the environment.
- Automatic operation: A CR system can autonomously perform the above adjustments without any user intervention.
- Adaptability: A CR system can adjust its own work mode and policies based on users’ behaviors and choices; and with reasoning and learning, it can better meet the users’ demands.
Figure 1 illustrates a typical CR technical framework, which is made up of two main parts: Cognitive Engine (CE) and Reconfigurable radio transceiver.
In implementing reconfiguration, SDR technology can play an important role. In an SDR system, the main parts of the radio transceiver can be defined with software; hence, the system can easily be reconfigured by means of software on the same hardware platform. This is significant in improving system reconfiguration efficiency and reducing life-cycle costs. However, SDR is not a necessary condition for the implementation of CR technology. In traditional hardware-based implementation architecture, the reconfiguration function can be partly realized with multi-mode and multiband hardware platforms.
CE defines the architecture and mechanism for a CR system to sense and analyze environmental information, as well as to reason and learn to adjust its own work mode. CR’s awareness and adaptive attributes form the core of a CR system. The main components of CE are an environmental monitor module, and a reasoning and learning module. The reasoning and learning module fulfills the “adaptive” feature of CR, while the environmental monitor module is the basic part of CE, fulfilling the “awareness” feature of the CR system. It senses wireless environment or obtains environmental information from other data sources. Environmental sensing, especially radio spectrum sensing, has always been a focus in CR field.
The purpose of radio spectrum sensing is to determine proper spectrum holes for communications. It operates without affecting the existing communication system. For a long time, spectrum sensing has relied on “all-around” radio terminals, which are used to scan spectrums and identify spectrum holes. Early sensing methods included pilot signal detection and cyclostationary feature detection. Simulations and analyses show that cooperative diversity technique can increase probability required by reliable detection. Cooperative sensing allows multiple cognitive users to exchange sensed information, thus dramatically improving spectrum interception and sensing capabilities. The cross-layer design for joint sensing between physical layer and Media Access Control (MAC) layer can also greatly enhance spectrum sensing capability.
Research achievements in last 10 years show that the wireless industry’s perspective on CR technology has changed, from “radio” to “network and system”. In the traditional “radio” perspective, the awareness, adjustability, adaptive and automatic attributes of a CR system require an “all-around” radio transceiver, which supports all modes and all bands. That is to say, the transceiver senses, discovers and negotiates suitable frequency, waveform and protocol. However, as the research deepens, limitations of this traditional perspective are revealed:
- The all-around transceiver is rarely produced in reality due to its cost, complexity and power consumption.
- This perspective only focuses on radio link sublayer functions, but ignores interaction and cooperation among different network nodes.
- The perspective does not reflect the user’s service and application requirements, nor their impact on wireless systems.
Therefore, the research perspectives on CR have gradually extended from radio link sublayer functions to higher-level protocol and network designs. Cognitive Wireless Networks (CWNs) have been commonly recognized as a development trend of CR technology in research and industrialization. CWNs adjust their network characteristics based on the information they acquire from the interaction with multi-dimension environments (networks, protocols, applications, etc.), thereby achieving optimal system performance.
2 Multi-Dimension Cooperative Communications
The history of mobile communication technologies has been a process of discovering and utilizing new radio resource dimensions to improve spectrum efficiency. Cellular technology in 1970s made frequency multiplexing possible and expanded the space of spectrum resources. The digitalization process beginning in 1980s enabled resources to be extended in time dimension. At the turn of the century, Multiple Input Multiple Output (MIMO) technology was introduced, marking the utilization of space dimension of resources. The discovery of these three resource dimensions has greatly driven the development of mobile communication technologies. In recent years, with many new technologies being introduced, the link performance in physical layer has been rapidly enhanced. Figure 2 shows physical layer link performance in the case of some typical 3.5G technologies. Figure 2 shows 3.5G mobile communication technologies, represented by Evolution Data Optimized (EV-DO), High-Speed Packet Access (HSPA) and IEEE 802.16, have achieved quite high link performance, almost reaching Shannon bound with 3dB margin. This means traditional technical approaches have little role in enhancing the link performance further. Cooperative communications in several resource dimensions is an effective means of enhancing the system. Table 1 summarizes cooperative elements, purposes and typical technical schemes of cooperative communications in different resource dimensions.
From Table 1, we can see that most cooperative communication technologies are based on full knowledge of contextual information such as user’ service types and radio environment characteristics. Only after contextual information is acquired, can intelligent cooperation among different elements be achieved. The sensing and collection of contextual information can only be done with CR and CWN technologies. Therefore, future multi-dimension mobile cooperative communications has a natural need for CR technologies.
3 The Combination of CR Technologies with Multi-Dimension Cooperation
In the development of CR and cooperative communications, both find that their own targets can be better achieved by absorbing the other’s technical ideas. As a result, the combination of the two technologies has become an inevitable trend.
3.1 Enhancing Cognitive Capability Through Cooperation
As mentioned above, traditional spectrum sensing technologies impose high requirement on radio transceivers: The transceivers should be all-around, supporting various radio bands and identifying diversified wireless access technologies. This requirement increases the complexity and cost of CR terminals to a great extent. In addition, large-scale spectrum sensing consumes a lot of time, which may lead to service latency and high power consumption of the terminal. In some special wireless communication environments, signal propagation suffers interference from multipath and shadow effects. Consequently, some cognitive users at special geographic locations may experience very low sensing probabilities, increasing the interference on licensed users. In this case, cooperative communication technology can considerably improve the efficiency in sensing spectrum and contextual information, enhancing sensing reliability and reducing the cost. Specifically, such cooperation can be further divided into two categories: cooperation between cognitive users and cooperation between users and the network.
Due to the presence of multipath and shadow effects in wireless channels, a single cognitive user may perform quite poorly in spectrum sensing in some special cases. For example, in Figure 3, cognitive user 1 senses and uses incorrect idle band as a result of shadow effect, so it brings about interference on a licensed user. One solution to the problem is to adopt a cooperative sensing approach. As indicated in Figure 3, by means of cooperative sensing of cognitive users 1 and 2, an idle band is accurately sensed despite shadow effect[5-6].
To solve the latency and power consumption problems that occur in large-scale spectrum sensing, cooperation between the network and cognitive users can be applied. This has become another strong research focus. The term Cognitive Pilot Channel (CPC) is proposed as a key activator (as shown in Figure 4), and it has attracted widespread attention in the wireless industry, both academic and applied.
In heterogeneous wireless networks, CPC supports reconfiguration management of the network and user terminals. The core idea of CPC is similar to the telephone book provided by a telecom operator. When a user registers a system or requests a service, it obtains such system information as local operators, wireless access technologies and frequencies through the CPC that is intercepting broadcast messages. It then adaptively decides which network, frequency and wireless access technology to use. Cooperation between the network and cognitive users can dramatically simplify spectrum sensing design of CR terminals, reduce the overheads and improve overall efficiency of the CR system. Moreover, the concept of CPC offers a new perspective on the possibility of integrated utilization of heterogeneous network resources[7-8].
3.2 Cognition-Based Cooperative Systems
The introduction of cognitive technology makes intelligent cooperative heterogeneous networks possible, and presses forwards full cooperation among elements of heterogeneous networks. Intelligent joint resource management can improve utilization of radio resources. To achieve joint resource management among heterogeneous networks, as well as network
self-organization and self-configuration, environmental information, and user terminal status and behavioral characteristics are necessary. In other words, without full knowledge of contextual information, radio resource management and self-organization/self-configuration of heterogeneous networks cannot be realized. With is in mind, CR is likely to be a critical technology. In cooperation among network layers, wireless access technologies and spectrum resources, network knowledge of users’ contextual information and intelligent interaction between the network and users will be critical in improving the system’s overall efficiency. Cognitive technology based on cooperation between the network and users will most likely play an increasingly important role. One application of such technology is CPC-based joint resource management framework proposed in Reference .
With basic information provided by CPC or other kinds of Common Spectrum Coordination Channels (CSCCs), cognitive terminals can work with the network to sense wireless contextual information, and return the sensed information and service traffic characteristics to the network. The network, in turn, will use this information for joint resource management among different wireless access technologies, for load sharing and mobility optimization.
Ultimately, it will achieve optimal system performance and provide and excellent user experience[10-17].
Both CR and multi-dimension cooperative communications are hot topics in the wireless industry. As physical layer link technologies suffer from a the bottleneck in performance improvement, multi-dimension cooperation among different network elements will certainly be an important approach to mobile communication system enhancement. Full knowledge of contextual information and users’ service characteristics are the bases for intelligent cooperation and joint resource management. Therefore, the combination of CR and cognitive networks with multi-dimension cooperative communications is of great significance theoretically and pragmatically, and it will be a focus of research, standardization and industrialization in the foreseeable future.
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The perspective on Cognitive Radio (CR) technology has turned to a "network and system" framework. To enhance a system’s cognitive capability and reduce cognitive costs, cooperative means must be adopted. As physical layer link technologies suffer from a bottleneck in performance improvement, multi-dimension cooperation among different network elements will certainly be a main approach to enabling mobile communication systems. Awareness of context and users’ service characteristics is the critical base for intelligent cooperation and joint resource management. Therefore, the combination of CR with multi-dimension cooperative communications has become an inevitable trend.